Business models for linked data¶
Model description¶
The models follow (see here and here):
Note that this diagram has been refined in a later post. However, it is rather a fine-grained analysis of the models presented. Actually, there is not any new model presented.
Comments per model follow:
1. Subsidized model¶
Under this model, a government entity or a non-profit organization has a funded mandate to deliver certain data to the public or their targeted constituency.
The profit is direct.
2. Subscription model¶
Some data will be valuable enough that you can charge people a subscription to access it. This model has been around for a while, but it will gain new life as linked data standards make it easier for people to consume and mash-up data in novel applications.
The problem is that open data is a key factor in linked data. How you are going to convince someone for paying to get open data? Maybe the key is the linking per se. That is: getting added value by triplifying open data, linking them and provide e.g., commercial sparql endpoints, and mash-up services. The profit is direct. E.g.: Open data about country cities/states, on its own, does not give any profit. Open data about companies and their waste, on its own does not give any profit. However, linking both data sets, and charge people a subscription to find wastes from companies in certain geographical areas gives profit.
This is also pointed out by Leigh Dodds.
For Linked Data to be most useful some of its needs to be free: you need to make at least a bare minimum of data freely available, e.g. to identify objects of interest, to enable annotation and linking, etc. In my opinion a freemium model is the core of any subscription model for Linked Data.
Other examples:All of the following assume that some basic element of the Linked Data is free, but that one is paying for:
1. Full Access
2. Timely Access
3. Archival Access
4. Block Access: paying for access to a dataset based on time, e.g. “for the next 24 hours”; or based on the number, frequency of accesses; or the number of concurrent accesses
5. Convenient Access: paying for access to the data through a specific mechanism. This might seem at odds with Linked Data, but its reasonable to assume that some organizations might want data feeds or dumps rather than on-line only access. This might come at a premium.
- Licencing: charge fees to use data. This is rare, since usually the added-value is what gives money.
- Microtransactions: on-demand payments for certain queries.
- Data marketplaces: expose Linked Data for free as a public service while charging for higher resolution datasets.
3. Advertising model¶
Advertising: the second oldest profession. Data-driven applications will have plenty of opportunity for contextual ads and sponsorships. One interesting twist will be advertisers who pay to include information in raw data feeds, data-layer ads if you will.
This is clear: pay for linking data with ad data. Example: I provide a linked data set for microRNAs target experimental results. I can earn money from companies that sell microarrays, whose ads can be related with the current data set using new properties that will related microRNA experiments with microarrays and their features that can support such experiments.
On the other hand, Leigh Dodds believes that it is not a viable model, since adding ads in data itself causes problems. Ads will be easily identified and if ads are easily identifiable, then they can be stripped out by apps.
It is difficult for a business to enforce that users of its Linked Data should display ads through its terms and conditions, e.g. requiring data-layer ads to be displayed in some form to users of an application. Also:
Adverts embedded into data is are not a useful way to distribute them to end-users. In an environment where adverts are increasingly profiled by a range of geographic, demographic or behavioural factors, incorporating blanket ads into data feeds loses all of that targetting capability. It also potentially loses the feedback, e.g. on click-throughs or impressions, that are useful for gauging the success of a campaign.
The profit is direct.
4. Authority model¶
If anyone can publish data on the web, how will you know what data is good? That problem will be an opportunity for third-party "authorities" to validate data — or do official reviews and certifications that are published as data — and charge for participation. Compliance services are related to this.
The profit is direct.
5. Affiliate model¶
Affiliate marketing programs generate over $6 billion/year in commissions and are a major source of transactions and leads for merchants such as Amazon.com. Embedding affiliate links in data, so that they are activated when surfaced into end-user applications, are a natural extension of this existing model.
The affiliate model is an electronic commerce business model that enables a firm to generate revenue streams on hundreds (even thousands) of items without carrying inventories, managing orders, processing payments, or handling packaging and shipping. In this arrangement, a website concentrates on a relationship with a very specific group of individuals as its core competence (see core competencies). It develops and continuously upgrades content and services to attract and retain the patronage of this group. Once it has a sizable number of regular visitors, it can generate revenue by carrying ads or links to merchants with products that its visitors seek or are interested in. Similar to advertising model, in the sense that you actually advertise the affiliates.
Examples:- Affiliate program: provide data streams to affiliates who distribute them in other applications in exchange for commissions on related sales.
- Affiliate participation: as an affiliate of other companies, combine affiliate product links with data to earn commissions on related sales.
The profit is indirect.
6. Value-Add model¶
Useful data can be bundled with other services to make the overall solution more valuable. For example, think of the benchmarking data now included with Google Analytics. Access to data can also be offered earlier in the sales funnel, as a lead generation incentive.
This is close to the subscription model.
This model is also pointed out in MediaShift Idea Lab:
It helps you build services based on your content.
It's difficult to get people to pay for news online, so news organizations will need to build services based on their news -- and other content -- that people will pay for. You could, for example, provide a service that enabled people to compare schools in different areas, based on inspection reports, league tables, news reports, and parents' stories. Creating services to do this is lots and lots easier if content is made machine-readable through linked data.
It allows you to link direct to source.
You're a news organization. Your brand is based partly on how much people trust the stuff you publish. Publishing in linked data enables you to link directly back to the report/research or statistics on which it was based -- especially if that source is itself linked data (such as this). That way, if you cite a crime statistic, say, you can link it directly back to the original source.
Examples:
- Tailored Push or Data Set Recommendation: find data sets tailored to user need. Specifically designed data sets are obtained either through advanced algorithms searching the Linked Data Cloud or by manually creation. These specialized data sets could be built by observing use queries that were made when using the analytics tools discussed previously. Imagine, if I’m examining data sets about beer imports from Holland to America and the next day an accurate break down by type of beer for the last 3 weeks appeared in my data inbox.
The profit is indirect.
7. Traffic model¶
As with Google Rich Snippets, data can be used to boost the visibility and ranking of sites in major and vertical search engines. This is data-enhanced search engine optimization (SEO++) to increase traffic. Nickname: the "data for nothing and links for free" model (apologies to Mark Knopfler).
See an example of a rich snippet. A rich snippet gives useful summary info about google search results at a glance (e..g, here avg review). Web masters should put RDFa statements in html pages for rich snippets to work.
This model is also pointed out in MediaShift Idea Lab:
Linked data can boost SEO (search engine optimization).
People who tell you they can boost your SEO usually sound like witch doctors, telling you to tag all sorts of hocus pocus that doesn't make rational sense or just seems like cynical populism. But at its simplest, SEO works through links. The more something is linked to, the higher it will rank in search results. So publishing content as linked data should, quite naturally, increase its SEO. A great example of this is the BBC's natural history output. Type "Lion" into Google and, chances are, a BBC linked data page will come in those results. This never used to happen until the BBC started tagging their natural history content as linked data.
This model is also pointed out by Leigh Dodds, considered as the top model 8-) at this moment:
The traffic model, with its indirect revenue generation by driving traffic to existing content and services, is well understood. The same model has been used to encourage organizations to open up Web APIs, so its natural to consider this for Linked Data also.
Because it is tried and tested it’s currently one of the strongest arguments for driving adoption of Linked Data, so I’d put this right at the top of the list. The feedback loop that is in place now with search engines makes that traffic generation a reality.
The profit is indirect.
8. Branding model¶
As Josh Jones-Dilworth said, "Data shapes conversations and markets." Data branding can use data — and the vocabularies that define and structure data — to position and promote a company's worldview and differentiation strategy.
The profit is indirect.
Arguments¶
- Particularly in the early days, most organizations will benefit from experimenting with linked data for traffic, branding, and a little value add. Their own value will be learning as much as anything. As the data web matures, and they become more experienced, they may embrace more direct revenue models.
- Interesting the post here.
What's missing from this list is a business model for the Linked Data switch. Entities that take in Linked Data, improve it or otherwise add value and reemit it as Linked Data have no solid business model to run on. Everyone active so far in the Linked Data business is either a data sink or a data source. To realize the full potential of Linked Data, there need to be viable switches, both collecting and emitting Linked Data.
References¶
- Business models for linked data and web 3.0
- Thoughts on Linked Data Business Models
- Another 5 Linked Data Business Models
- 8 One-Way Business Models for Linked Data
- The Evolution of Linked Data Business Models
- Linked Data in Enterprises – some ideas for business models
- Where is the business value in Linked Data